Sample cutting method for imbalanced text sentiment classification based on BRC-Reference-Cited by-同舟云学术

Sample cutting method for imbalanced text sentiment classification based on BRC

Published:2013-01 Issue: Volume:37 Page:451-461
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Wang Suge,Li Deyu,Zhao Lidong,Zhang Jiahao

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference53 articles.

1. Y. Yang, Sampling strategies and learning efficiency in text categorization, in: Proceedings of the AAAI Spring Symposium on Machine Learning in Information Access, 1996, pp. 88–95.

2. P. Chaovalit, L.N. Zhou, Movie review mining: a comparison between supervised and unsupervised classification approaches, in: Proceedings of the 38th Hawaii International Conference on System Sciences, Big Island, Hawaii, pp. 1–9.

3. A. Kennedy, D. Inkpen, Sentiment classification of movie and product reviews using contextual valence shifters, in: Workshop on the Analysis of Informal and Formal Information Exchange During Negotiations, 2005.

4. G. Michael, Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis, in: Proceedings the 20th International Conference on Computational Linguistics, 2004.

5. B. Pang, L. Lee, S. Vaithyanathan, Thumbs Up? Sentiment Classification Using Machine Learning Techniques, EMNLP, 2002.

Cited by 28 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Multi-dimensional Survey on Learning from Imbalanced Data;Lecture Notes in Networks and Systems;2024

2. Analysis and Prediction of E-Bank Suspicious Accounts Based on Ensemble Learning Under Imbalance Data;Learning and Analytics in Intelligent Systems;2023

3. Hotel Review Sentiment Analysis Using Indonesian Language Based on Machine Learning;2022 International Conference on Electrical Engineering, Computer and Information Technology (ICEECIT);2022-11-22

4. A network-based feature extraction model for imbalanced text data;Expert Systems with Applications;2022-06

5. A novel textual data augmentation method for identifying comparative text from user-generated content;Electronic Commerce Research and Applications;2022-05